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Spatiotemporal filtering for regional GPS network in China using independent component analysis

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Abstract

Removal of the common mode error (CME) is a routine procedure in postprocessing regional GPS network observations, which is commonly performed using principal component analysis (PCA). PCA decomposes a network time series into a group of modes, where each mode comprises a common temporal function and corresponding spatial response based on second-order statistics (variance and covariance). However, the probability distribution function of a GPS time series is non-Gaussian; therefore, the largest variances do not correspond to the meaningful axes, and the PCA-derived components may not have an obvious physical meaning. In this study, the CME was assumed statistically independent of other errors, and it was extracted using independent component analysis (ICA), which involves higher-order statistics. First, the ICA performance was tested using a simulated example and compared with PCA and stacking methods. The existence of strong local effects on some stations causes significant large spatial responses and, therefore, a strategy based on median and interquartile range statistics was proposed to identify abnormal sites. After discarding abnormal sites, two indices based on the analysis of the spatial responses of all sites in each independent component (east, north, and vertical) were used to define the CME quantitatively. Continuous GPS coordinate time series spanning \(\sim \)4.5 years obtained from 259 stations of the Tectonic and Environmental Observation Network of Mainland China (CMONOC II) were analyzed using both PCA and ICA methods and their results compared. The results suggest that PCA is susceptible to deriving an artificial spatial structure, whereas ICA separates the CME from other errors reliably. Our results demonstrate that the spatial characteristics of the CME for CMONOC II are not uniform for the east, north, and vertical components, but have an obvious north–south or east–west distribution. After discarding 84 abnormal sites and performing spatiotemporal filtering using ICA, an average reduction in scatter of 6.3% was achieved for all three components.

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References

  • Aires F, Rossow WB, Chédin A (2002) Rotation of EOFs by the independent component analysis: toward a solution of the mixing problem in the decomposition of geophysical time series. J Atmos Sci 59:111–123. doi:10.1175/1520-0469(2002)059<0111:ROEBTI>2.0.CO

    Article  Google Scholar 

  • Altamimi Z, Collilieux X, Métivier L (2011) ITRF2008: an improved solution of the international terrestrial reference frame. J Geod 85:457–473. doi:10.1007/s00190-011-0444-4

    Article  Google Scholar 

  • Amiri-Simkooei AR (2009) Noise in multivariate GPS position time-series. J Geod 83:175–187. doi:10.1007/s00190-008-0251-8

    Article  Google Scholar 

  • Asuero AG, Sayago A, González AG (2006) The correlation coefficient: an overview. Crit Rev Anal Chem 36:41–59. doi:10.1080/10408340500526766

    Article  Google Scholar 

  • Beavan J (2005) Noise properties of continuous GPS data from concrete pillar geodetic monuments in New Zealand and comparison with data from U.S. deep drilled brace monuments. J. Geophys Res 110(B08410). doi:10.1029/2005JB003642

  • Bevis M, Brown A (2014) Trajectory models and reference frames for crustal motion geodesy. J Geod 88:283–311. doi:10.1007/s00190-013-0685-5

    Article  Google Scholar 

  • Bin L, Dai W, Wei P, Xiaoling M (2015) Spatiotemporal analysis of GPS time series in vertical direction using independent component analysis. Earth Planets Space 67(1):1–10. doi:10.1186/s40623-015-0357-1

    Article  Google Scholar 

  • Blewitt G, Kreemer C, Hammond WC, Goldfarb JM (2013) Terrestrial reference frame NA12 for crustal deformation studies in North America. J Geodyn 72:11–24. doi:10.1016/j.jog.2013.08.004

    Article  Google Scholar 

  • Boergens E, Rangelova E, Sideris MG, Kusche J (2014) Assessment of the capabilities of the temporal and spatiotemporal ICA method for geophysical signal separation in GRACE data. J Geophys Res Solid Earth 119:4429–4447. doi:10.1002/2013JB010452

    Article  Google Scholar 

  • Bottiglieri M, Falanga M, Tammaro U et al (2007) Independent component analysis as a tool for ground deformation analysis. Geophys J Int 168:1305–1310. doi:10.1111/j.1365-246X.2006.03264.x

    Article  Google Scholar 

  • Bottiglieri M, Falanga M, Tammaro U et al (2010) Characterization of GPS time series at the Neapolitan volcanic area by statistical analysis. J Geophys Res Solid Earth 115:n/a–n/a. doi:10.1029/2009JB006594

  • Carr Agnew D (2013) Realistic simulations of geodetic network data: the Fakenet package. Seismol Res Lett 84:426–432. doi:10.1785/0220120185

    Article  Google Scholar 

  • Comon P (1994) Independent component analysis, a new concept? Signal Process 36(3):287–314

    Article  Google Scholar 

  • Dai W, Huang D, Cai C (2014) Multipath mitigation via component analysis methods for GPS dynamic deformation monitoring. GPS Solut 18:417–428. doi:10.1007/s10291-013-0341-9

    Article  Google Scholar 

  • Dmitrieva K, Segall P, DeMets C (2015) Network-based estimation of time-dependent noise in GPS position time series. J Geod 89:591–606. doi:10.1007/s00190-015-0801-9

    Article  Google Scholar 

  • Dong D, Fang P, Bock Y et al (2002) Anatomy of apparent seasonal variations from GPS-derived site position time series. J Geophys Res Solid Earth 107:n/a–n/a. doi:10.1029/2001JB000573

  • Dong D, Fang P, Bock Y et al (2006) Spatiotemporal filtering using principal component analysis and Karhunen–Loeve expansion approaches for regional GPS network analysis. J Geophys Res Solid Earth 111:n/a–n/a. doi:10.1029/2005JB003806

  • Forootan E, Awange JL, Kusche J et al (2012) Independent patterns of water mass anomalies over Australia from satellite data and models. Remote Sens Env 124:427–443. doi:10.1016/j.rse.2012.05.023

    Article  Google Scholar 

  • Forootan E, Kusche J (2012) Separation of global time-variable gravity signals into maximally independent components. J Geod 86:477–497. doi:10.1007/s00190-011-0532-5

    Article  Google Scholar 

  • Frappart F, Ramillien G, Maisongrande P (2010) Denoising satellite gravity signals by independent component analysis. IEEE Geosci Remote Sensen Lett 7:421–425

    Article  Google Scholar 

  • Frappart F, Ramillien G, Leblanc M et al (2011) An independent component analysis filtering approach for estimating continental hydrology in the GRACE gravity data. Remote Sens Env 115:187–204. doi:10.1016/j.rse.2010.08.017

    Article  Google Scholar 

  • Gazeaux J, Williams S, King M et al (2013) Detecting offsets in GPS time series: first results from the detection of offsets in GPS experiment. J Geophys Res Solid Earth 118:2397–2407. doi:10.1002/jgrb.50152

    Article  Google Scholar 

  • Gualandi A, Serpelloni E, Belardinelli ME (2016) Blind source separation problem in GPS time series. J Geod 90(4):323–341. doi:10.1007/s00190-015-0875-4

    Article  Google Scholar 

  • He X, Hua X, Yu K et al (2015) Accuracy enhancement of GPS time series using principal component analysis and block spatial filtering. Adv Space Res 55:1316–1327. doi:10.1016/j.asr.2014.12.016

    Article  Google Scholar 

  • Herring TA, King RW, McClusky SC (2010) Documentation of the GAMIT and GLOBK software release 10.4. Mass Inst of Technol, Cambridge

  • Hyvärinen A (1999a) Fast and robust fixed-point algorithms for independent component analysis. IEEE Transact Neural Netw 10:626–634. doi:10.1109/72.761722

    Article  Google Scholar 

  • Hyvärinen A (1999b) Survey on independent component analysis. Neural Comput Surv 2:94–128

    Google Scholar 

  • Hyvärinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 13:411–430

    Article  Google Scholar 

  • Hyvärinen A, Karhunen J, Oja E (2001) Independent component analysis. Wiley, New York

    Book  Google Scholar 

  • Ji KH, Herring TA (2011) Transient signal detection using GPS measurements: Transient inflation at Akutan volcano, Alaska, during early 2008. Geophys Res Lett 38: n/a–n/a. doi:10.1029/2011GL046904

  • Langbein J (2008) Noise in GPS displacement measurements from Southern California and Southern Nevada. J Geophys Res Solid Earth 113:n/a–n/a. doi:10.1029/2007JB005247

  • Ledesma RD, Valero-mora P (2007) Determining the number of factors to retain in EFA: an easy-to- use computer program for carrying out parallel analysis. Pract Assess Res Eval 12:1–11

    Google Scholar 

  • Li W, Shen Y, Li B (2015) Weighted spatiotemporal filtering using principal component analysis for analyzing regional GNSS position time series. Acta Geodaetica et Geophysica 1–18: doi:10.1007/s40328-015-0100-1

  • Luo F, Dai W, Tang C et al (2012) EMD-ICA with reference signal method and its application in GPS multipath. Acta Geod Cartogr Sin 41(3):366–371

    Google Scholar 

  • Mao A, Harrison GA, Dixon H et al (1999) Noise in GPS coordinate time series. J Geophys Res 104:2797–2816. doi:10.1029/1998jb900033

    Article  Google Scholar 

  • Márquez-Azúa B, DeMets C (2003) Crustal velocity field of Mexico from continuous GPS measurements, 1993 to June 2001: implications for the neotectonics of Mexico. J Geophys Res Solid Earth. doi:10.1029/2002JB002241

  • Ming F, Yang Y, Zeng A et al (2016) Detecting offsets in GPS position time series considering colored noises. Geomat Inf Sci Wuhan Univ 41(6):745–751. doi:10.13203/j.whugis20140603

    Google Scholar 

  • Nikolaidis R (2002) Observation of geodetic and seismic deformation with the global positioning system. University of California, San Diego

    Google Scholar 

  • Peres-Neto PR, Jackson DA, Somers KM (2005) How many principal components? Stopping rules for determining the number of non-trivial axes revisited. Comput Stat Data Anal 49:974–997. doi:10.1016/j.csda.2004.06.015

    Article  Google Scholar 

  • Schneider T (2001) Analysis of incomplete climate data: estimation of mean values and covariance matrices and imputation of missing values. J Clim 14:853–871. doi:10.1175/1520-0442(2001)014<0853:AOICDE>2.0.CO;2

    Article  Google Scholar 

  • Serpelloni E, Faccenna C, Spada G et al (2013) Vertical GPS ground motion rates in the Euro-Mediterranean region: new evidence of velocity gradients at different spatial scales along the Nubia-Eurasia plate boundary. J Geophys Res Solid Earth 118:6003–6024. doi:10.1002/2013JB010102

    Article  Google Scholar 

  • Shen Y, Li W, Xu G (2014) Spatiotemporal filtering of regional GNSS network’s position time series with missing data using principle component analysis. J Geod 88:1–12. doi:10.1007/s00190-013-0663-y

    Article  Google Scholar 

  • Sheng C, Gan W, Liang S et al (2014) Identification and elimination of non-tectonic crustal deformation caused by land water from GPS time series in the western Yunnan province based on GRACE observations. Chin J Geophys 57(1):42–52. doi:10.6038/cjg20140105

    Google Scholar 

  • Stone JV (2004) Independent component analysis: a tutorial introduction. The MIT Press, Cambridge

    Google Scholar 

  • Székely G, Rizzo M (2007) Measuring and testing independence by correlation distances. Ann Stat 35:2769–2794

    Article  Google Scholar 

  • Tian Y, Shen Z-K (2016) Extracting the regional common-mode component of GPS station position time series from dense continuous network. J Geophys Res Solid Earth 121: doi:10.1002/2015JB012253

  • Tian Y, Shen Z (2011) Correlation weighted stacking filtering of common-mode component in GPS observation network. Acta Seismol Sin 33(2):198–208

    Google Scholar 

  • van Dam TM, Blewitt G, Hefilin M (1994) Detection of atmospheric pressure loading using the global positioning system. J Geophys Res 99:23929–23950

    Google Scholar 

  • van Dam TM, Whar J, Milly PCD, Shmakin AB, Blewitt G, Levallée D, Larson K et al (2001) Crustal displacements due to continental water loading. Geophys Res Lett 28:651–654

    Article  Google Scholar 

  • van Dam TM, Collilieux X, Wuite J, Altamimi Z, Ray J (2012) Nontidal ocean loading effects in GPS height time series. J Geod 86(11):1043–1057. doi:10.1007/s00190-012-0564-5

    Article  Google Scholar 

  • van Dam TM, Whar J (1987) Displacements of the Earth’s surface due to atmospheric loading: effects on gravity and baseline measurements. J Geophys Res 92:1281–1286

    Article  Google Scholar 

  • Wang M, Li Q, Wang F et al (2011) Far-field coseismic displacements associated with the 2011 Tohoku-oki earthquake in Japan observed by global positioning system. Chin Sci Bull 56:2419–2424. doi:10.1007/s11434-011-4588-7

    Article  Google Scholar 

  • Wang W, Zhao B, Wang Q et al (2012) Noise analysis of continuous GPS coordinate time series for CMONOC. Adv Space Res 49:943–956. doi:10.1016/j.asr.2011.11.032

    Article  Google Scholar 

  • Wdowinski S, Bock Y, Zhang J et al (1997) Southern California permanent GPS geodetic arrary: spatial filtering of daily positions for estimating coseismic and postseismic displacements induced by the 1992 Landers earthquake. J Geophys Res 102:18057–18070

    Article  Google Scholar 

  • Williams SDP, Bock Y, Fang P et al (2004) Error analysis of continuous GPS position time series. J Geophys Res Solid Earth. doi:10.1029/2003JB002741

  • Xie S, Pan P, Zhou X (2014) Research on common mode error xtraction method for large-scale GPS network. Geomat Inf Sci Wuhan Univ 39(10):1168–1173

    Google Scholar 

  • Yang Y, Song L, Tianhe Xu (2002) Robust estimator for correlated observations based on bifactor equivalent weights. J Geod 76(6–7):353–358

    Article  Google Scholar 

  • Yanqiang W, Jiang Z, Wang M et al (2013) Preliminary results of the co-seismic displacement and pre-seismic strain accumulation of the Lushan MS7.0 earthquake reflected by the GPS surveying. Chin Sci Bull 58:3460–3466. doi:10.1007/s11434-013-5998-5

    Article  Google Scholar 

  • Zhang J, Bock Y, Johnson H et al (1997) Southern California permanent GPS geodetic array: error analysis of daily position estimates and site velocities. J Geophys Res 102:18035–18055

    Article  Google Scholar 

  • Zhao B, Du R, Zhang R et al (2015a) Co-seismic displacements associated with the 2015 Nepal Mw7.9 earthquake and Mw7.3 aftershock constrained by Global Positioning System Measurements. Chin Sci Bull 60(28–29):2758–2764

  • Zhao B, Huang Y, Zhang C et al (2015b) Crustal deformation on the Chinese mainland during 1998–2014 based on GPS data. Geod Geodyn 6(1):7–15. doi:10.1016/j.geog.2014.12.006

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Acknowledgements

We thank the Crustal Movement Observation Network of China (CMONOC) (http://www.cgps.ac.cn) for providing GPS data. We would like to thank Peng Fang for comments on a draft version of this manuscript. We would also like to thank Dr. Dong (East China Normal University) for providing the QOCA software and helpful discussions. We are thankful to Dr. Tian (Key Laboratory of Crustal Dynamics, Institute of Crustal Dynamics, China Earthquake Administration) for the intense discussion about GPS data processing. The authors would also like to thank two anonymous reviewers and the editor T. van Dam for their insightful comments and suggestions, which help to improve the manuscript significantly. The Generic Mapping Tool (GMT) software package was used to plot the figures. GPS data were processed using the GAMIT/GLOBK software.     This study was funded by the Key R&D Program (Grant no. 2016YFB0501701), National Natural Science Foundation of China (Grant nos. 41604013, 41374019, 41474015), and Funded by State Key Laboratory of Geo-information Engineering, No. SKLGIE2015-Z-1-1.

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Authors and Affiliations

  1. Institute of Geospatial Information, Information Engineering University, No.62, Kexue Road, Zhengzhou, 450001, China

    Feng Ming

  2. National Key Laboratory of Geo-information Engineering, Xi’an Institute of Surveying and Mapping, Xi’an, 710054, China

    Feng Ming, Yuanxi Yang & Anmin Zeng

  3. Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan, 430071, China

    Bin Zhao

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Correspondence to Feng Ming.

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Ming, F., Yang, Y., Zeng, A. et al. Spatiotemporal filtering for regional GPS network in China using independent component analysis. J Geod 91, 419–440 (2017). https://doi.org/10.1007/s00190-016-0973-y

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